Clustering with H2O

Explore the fundamentals of clustering with H2O, including distance measures and popular algorithms. Understand how to implement K-Means, hierarchical clustering, and DBSCAN to group similar data points. Learn parameter tuning and model prediction to identify meaningful clusters and analyze unsupervised data effectively.

We'll cover the following...

Introduction to clustering models
- Approaches to computing similarity
H2OKMeansEstimator
- Estimating k in K-Means
- H2OKMeansEstimator parameters

Introduction to clustering models

Unsupervised clustering models group similar data points based on their inherent patterns or similarities. Their goal is to find natural groupings within the data without any predefined labels or categories.

To achieve well-defined clusters, the algorithm measures the similarity or distance between data points and groups that are close together. The goal of unsupervised clustering is to maximize intracluster similarity while maximizing intercluster dissimilarity. In other words, we want the data points within a cluster to be similar to each other, and at the same time, we want the clusters to be distinct from other clusters.

By optimizing these two factors, unsupervised clustering algorithms can identify meaningful and well-separated clusters in the data. They allow us ...

1.Introduction to Machine Learning

2.Supervised Learning: Regression Models with H2O

3.Supervised Learning: Classification Models with H2O

4.Unsupervised Learning: Clustering with H2O

5.Unsupervised Learning: Anomaly Detection with H2O

6.Closing Notes

7.Appendix

Clustering with H2O

Introduction to clustering models